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bert-base-nsmc – AI Model by sanga12 | AlphaNeural AI
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sanga12
/
bert-base-nsmc
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transformers
tf
bert
text-classification
generated_from_keras_callback
klue/bert-base
finetune
cc-by-sa-4.0
autotrain_compatible
endpoints_compatible
us
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bert-base-nsmc
This model is a fine-tuned version of
klue/bert-base
on an unknown dataset. It achieves the following results on the evaluation set:
Train Loss: 0.0247
Train Accuracy: 0.9927
Validation Loss: 0.5184
Validation Accuracy: 0.8772
Epoch: 4
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1058, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 117, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': np.float32(0.9), 'beta_2': np.float32(0.999), 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.1}
training_precision: float32
Training results
Train Loss
Train Accuracy
Validation Loss
Validation Accuracy
Epoch
0.3956
0.8128
0.3073
0.8704
0
0.2123
0.9173
0.3292
0.8662
1
0.1016
0.9648
0.3824
0.8756
2
0.0455
0.9858
0.4747
0.8778
3
0.0247
0.9927
0.5184
0.8772
4
Framework versions
Transformers 4.55.2
TensorFlow 2.19.0
Datasets 4.0.0
Tokenizers 0.21.4
